Automated damage detection is an integral component of each structural health monitoring (SHM) system. Typically, measurements from various sensors are collected and reduced to damage-sensitive features, and diagnostic values are generated by statistically evaluating the features. Since changes in data do not only result from damage, it is necessary to determine the confounding factors (environmental or operational variables) and to remove their effects from the measurements or features. Many existing methods for correcting confounding effects are based on different types of mean regression. This neglects potential changes in higher-order statistical moments, but in particular, the output covariances are essential for generating reliable diagnostics for damage detection. This article presents an approach to explicitly quantify the changes in the covariance, using conditional covariance matrices based on a non-parametric, kernel-based estimator. The method is applied to the Munich Test Bridge and the KW51 Railway Bridge in Leuven, covering both raw sensor measurements (acceleration, strain, inclination) and extracted damage-sensitive features (natural frequencies). The results show that covariances between different vibration or inclination sensors can significantly change due to temperature changes, and the same is true for natural frequencies. To highlight the advantages, it is explained how conditional covariances can be combined with standard approaches for damage detection, such as the Mahalanobis distance and principal component analysis. As a result, more reliable diagnostic values can be generated with fewer false alarms.

翻译：自动损伤检测是每个结构健康监测（SHM）系统的核心组成部分。通常，系统会收集来自各类传感器的测量数据，将其约简为损伤敏感特征，并通过统计评估这些特征来生成诊断值。由于数据变化不仅源于损伤，因此有必要确定混杂因素（环境或运行变量），并将其影响从测量数据或特征中移除。现有许多校正混杂效应的方法基于不同类型的均值回归。这种做法忽略了高阶统计矩的潜在变化，而输出协方差对于生成可靠的损伤检测诊断值尤为重要。本文提出一种方法，基于非参数核估计器计算条件协方差矩阵，从而显式量化协方差的变化。该方法应用于慕尼黑试验桥和鲁汶的KW51铁路桥，涵盖原始传感器测量数据（加速度、应变、倾角）以及提取的损伤敏感特征（固有频率）。结果表明，不同振动或倾角传感器间的协方差会因温度变化而发生显著改变，固有频率亦如此。为突显其优势，本文阐释了如何将条件协方差与损伤检测的标准方法（如马氏距离和主成分分析）相结合。由此可在减少误报的同时生成更可靠的诊断值。